DCAgent/FourDatasetMixQwen3_8B
DCAgent/FourDatasetMixQwen3_8B is an 8 billion parameter language model fine-tuned from the Qwen/Qwen3-8B architecture. This model was specifically fine-tuned on the otagents_10k dataset, suggesting an optimization for tasks related to agentic behavior or specific conversational patterns found within that dataset. With a context length of 32768 tokens, it is designed for applications requiring processing of moderately long sequences.
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Model Overview
DCAgent/FourDatasetMixQwen3_8B is an 8 billion parameter language model derived from the Qwen/Qwen3-8B base architecture. It has undergone fine-tuning using the otagents_10k dataset, indicating a specialization for tasks or interactions represented in this specific dataset. The model supports a substantial context length of 32768 tokens, making it suitable for processing and generating longer text sequences.
Training Details
The fine-tuning process utilized the following key hyperparameters:
- Learning Rate: 4e-05
- Batch Size: A total effective batch size of 16 (1 train_batch_size with 4 gradient_accumulation_steps across 4 GPUs).
- Optimizer: AdamW with specific beta values (0.9, 0.98) and epsilon (1e-08).
- Scheduler: Cosine learning rate scheduler with a 0.1 warmup ratio.
- Epochs: Trained for 5.0 epochs.
Potential Use Cases
Given its fine-tuning on the otagents_10k dataset, this model is likely best suited for applications that align with the characteristics and content of that dataset. Developers should evaluate its performance for tasks involving agent-like interactions, specific dialogue systems, or data generation within the domain covered by otagents_10k.